docstr: Return privacy budget (epsilon) expended so far.
  
  Args:
      delta: target delta
      poisson: ``True`` is input batches was sampled via Poisson sampling,
          ``False`` otherwise
docstr_tok: ["Return","privacy","budget","(","epsilon",")","expended","so","far",".","Args",":","delta",":","target","delta","poisson",":","``","True","``","is","input","batches","was","sampled","via","Poisson","sampling",",","``","False","``","otherwise"]
code: def get_epsilon(self, delta: float, poisson: bool = True) -> float:
      """
      Return privacy budget (epsilon) expended so far.
  
      Args:
          delta: target delta
          poisson: ``True`` is input batches was sampled via Poisson sampling,
              ``False`` otherwise
      """
  
      compute_eps = (
          privacy_analysis.compute_eps_poisson
          if poisson
          else privacy_analysis.compute_eps_uniform
      )
      noise_multiplier, sample_rate, num_steps = self.history[-1]
      return compute_eps(
          steps=num_steps,
          noise_multiplier=noise_multiplier,
          sample_rate=sample_rate,
          delta=delta,
      )
  
file_path: opacus/opacus/accountants/gdp.py
imports: import warnings
  from .accountant import IAccountant
  from .analysis import gdp
label_desc: Use image node_id 3 for calling the GaussianAccountant obj's underlying member method code with example usage: obj.get_epsilon(delta, poisson) and returns: compute_eps